ALSO is an anomaly detection algorithm that uses predictive models to produce outlier scores. For a given dataset, separate models are fit for each feature. In each model, one feature is the target and the remaining features are predictors. Observations that consistently deviate from expected (predicted) values across the individual models are scored highly and may represent outliers/anomalies. This package uses random forests (from the ranger package) to build individual regressors and/or classifiers.